Machine Learning and Speech: A Review of FPF’s Digital Data Flows Masterclass

Authors: Hunter Dorwart, Caroline Hopland, and Rob van Eijk On Wednesday, December 9, 2020, FPF hosted a Digital Data Flows Masterclass on Machine Learning and Speech.

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Future of Privacy Forum

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https://fpf.org/2020/12/18/machine-learning-and-speech-a-review-of-fpfs-digital-data-flows-masterclass/

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Friday, December 18, 2020

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Articles, Academic disciplines, Learning, Artificial intelligence, Emerging technologies, Artificial neural networks, Machine learning, Deep learning, Unsupervised learning, Supervised learning, Machine translation, Semi-supervised learning

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Authors: Hunter Dorwart, Caroline Hopland, and Rob van Eijk On Wednesday, December 9, 2020, FPF hosted a Digital Data Flows Masterclass on Machine Learning and Speech.
The masterclass on Machine Learning and Speech is the first masterclass of a new series after completing the VUB-FPF Digital Data Flows Masterclass series with eight topics.
Dr. Prem Natarajan (VP, Alexa AI-Natural Understanding) guides us through the intricacies of Machine Learning in the context of voice assistants.
The presenters explored the differences between supervised, semi-supervised, and unsupervised machine learning (ML) and the impact of these recent technical developments on machine translation and speech recognition technologies.
Machine Translation An Introduction As a sub-field of computational linguistics, machine translation studies the use of software to translate text or speech from one language to another.
Deep learning requires very large datasets and paired translation examples (e.g., English to French), which might not exist for certain languages.
Even when there is a large pool of data, sentences often have multiple correct translations, which poses additional problems for machine learning.
How to Train a Machine Learning Model Supervised, Unsupervised, and Semi-Supervised Learning Training neural models with limited translation data poses multiple problems (see also, Figure 1).
Finally, semi-supervised learning combines elements of both supervised and unsupervised learning and is the primary training method for translation models.
From a high level perspective, neural models employing machine learning produce more fluent translations than older statistical models.
Machine Learning and Speech Many of the lessons learned in recent advancements of machine translation also apply to the area of speech recognition.
Yet while the idea of machine learning through neural networking became widespread in the discipline, the lack of computing power and data inhibited its development.
Advancements in Training Models Indeed, present speech recognition technology utilizes a range of learning methods at the cutting edge of deep learning, including active learning, transfer learning, semi-supervised learning, learning to paraphrase, self-learning, and teaching AI.
Transfer learning data takes data rich domains and finds ways to transfer learning to new domains that lack corresponding data.
Digital Data Flows Masterclass Archive The Digital Data Flows Masterclass Archive with the recordings and resources for previous classes can be accessed here.